Learning Bayesian Networks from Dependency Networks: A Preliminary Study
Abstract
In this paper we describe how to learn Bayesian networks from a summary of complete data in the form of a dependency network rather than from data directly. This method allows us to gain the advantages of both representations: scalable algorithms for learning dependency networks and convenient inference with Bayesian networks. Our approach is to use a dependency network as an "oracle" for the statistics needed to learn a Bayesian network. We show that the general problem is NP-hard and develop a greedy search algorithm. We conduct a preliminary experimental evaluation and find that the prediction accuracy of the Bayesian networks constructed from our algorithm almost equals that of Bayesian networks learned directly from the data.
Cite
Text
Hulten et al. "Learning Bayesian Networks from Dependency Networks: A Preliminary Study." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.Markdown
[Hulten et al. "Learning Bayesian Networks from Dependency Networks: A Preliminary Study." Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics, 2003.](https://mlanthology.org/aistats/2003/hulten2003aistats-learning/)BibTeX
@inproceedings{hulten2003aistats-learning,
title = {{Learning Bayesian Networks from Dependency Networks: A Preliminary Study}},
author = {Hulten, Geoff and Chickering, David Maxwell and Heckerman, David},
booktitle = {Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics},
year = {2003},
pages = {141-148},
volume = {R4},
url = {https://mlanthology.org/aistats/2003/hulten2003aistats-learning/}
}